Modeling Trading Costs with Commissions and Slippage
Summary
The document distinguishes two practical components of trading costs: broker commissions and slippage. Commissions are described as charges per share, while slippage reflects execution prices that differ from the market and depends on trading volume, order type, and execution method. The response uses liquid stocks traded algorithmically below a modest share of daily volume as an example of conditions where costs may be relatively low.
The main modeling lesson is that a uniform proportional cost, such as the academic 50 basis point assumption raised in the question, may not represent actual execution costs across trading conditions. Cost estimates should reflect liquidity, participation, and execution choices, using broker information where available. When reliable estimates are unavailable, the response suggests assessing strategy P&L across a range of cost assumptions. The figures are illustrative lower-bound estimates from the answer, not universal rates; costs can rise for less liquid assets or larger participation.
Key ideas
- Trading costs include commissions and slippage, which respond to different factors.
- Per-share commissions depend on the broker and whether execution is algorithmic or discretionary.
- Slippage varies with liquidity, participation in market volume, and order type.
- A single proportional cost assumption may not fit different trading conditions.
- Testing strategy P&L across several cost assumptions can show how sensitive it is to trading costs.
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Full text
# How large are transaction costs in practice?
# How large are transaction costs in practice?
I am wondering, what kind of transaction costs practitioners (institutional investors) are faced to. Portfolio optimization literature often evaluates portfolio performance after adjusting for a value taking the form $T(\Delta) = 50/10.000 \sum_{i=1}^N |\Delta_i|$ where $\Delta_i$ is rebalancing of wealth in asset $i$. I do not expect this number to reflect what is really going on in the markets, however, due to the lack of better approximations the functional form above is used frequently in academia.
- Does the proportionality constant of $50$ bp has some reliability in the industry?
- Do investors in reality face a fixed fee as soon as they touch a single asset? In other words, given I rebalance a small amount $\varepsilon>0$, am I going to by a fee anyway just because I called my broker?
- The form above also assumes that transaction costs keep smooth even I rebalance a lot of my wealth. Does this approximation hold in reality, for example by performing some form of smart order routing?
I am happy for every reply or reference coming up with new ideas on how one could take into consideration transaction costs!
## Answer by user18489 (score 4, accepted)
https://quant.stackexchange.com/a/30819
There are two types of costs one occurs while trading: commission fees and slippage. Commission fees (fixed amount paid to broker) are a based on a per share basis (amount per share x shares traded) while slippage (% worse/better execution than market) is a function of participation to total trading volume as well as the order type. Commission fee also depends on execution i.e. algorithm vs. discretionary. If you trade very liquid stocks,using algorithmic execution (i.e. VWAP) and trade below 3-5% of daily volume then commission fees can be $0.0015 per share and slippage <20bps. Think of this as your lower bound. If you trade illiquid stocks or bigger part of daily volume then slippage will increase. 50bps seems arbitrary, so unless you know the above conditions (trading volume, order type, execution venue) or you are able get some numbers from the brokers in order to model slippage, it would be more useful to find pnl decay to various costs assumptions and then think whether the strategy makes sense or not.Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.